The Earnix Blog

The Earnix Blog is a community blog for financial industry professionals that share a passion for predictive analytics

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30
January

Customer Experience and Machine Learning: Future Roadmaps

In my first blog post on the topic – Customer Experience and Machine Learning: Practical Applications – I discussed how machine learning techniques are being used today by financial services organizations to achieve business benefit. Insurers and retail banks are using machineRead More »
04
January

Customer Experience and Machine Learning: Practical Applications

The notion of using analytics to improve customer experience has changed the landscape and thought process of businesses over the past several years. As machine learning becomes further democratized, or more pervasively available, it is making its way into many enterprise softwareRead More »
29
June

Are You Making these 7 Mistakes When Deploying Your Models?

I am always excited to see more organizations wanting to use analytical models at the center of their business processes. Like most things in life, the road to success is not straight; sometimes there are a few road bumps to navigate. TheRead More »
15
June

How Insurers Can Better Understand Multi-Product Customer Behavior – Machine Learning Can Help

It’s all about the data. That’s the message I kept hearing at the CAS Ratemaking and Project Management conference in San Diego this spring. The message was so strong, that I was asked to present my seminar, “Multi-Product Optimization: Challenges and Opportunities”Read More »
30
May

Conversion & Retention Modeling Done Right: The Four Keys to Success

These days, many industries are spending millions of dollars in research to achieve their customer-centric goals. The insurance industry is no different. Insurers are continually raising the analytical bar to gain a better understanding of their customers. Being able to accurately predictRead More »
29
March

How to Transform Your Models into a Fully-integrated Analytics System

More than ever before, financial service organizations are using analytical models to power their core business processes. Ever-higher customer expectations together with new market players are some of the drivers that are responsible for accelerating the pace of change in the market.Read More »
16
February

A Solid Analytical Solution: Operational Excellence

Over the last month or so, I have read numerous blogs that deal with forward-looking trends for 2017. While each blog has its angle, one cannot miss the huge role being placed on data analysis and analytics. As such, at the endRead More »
31
January

Using Conversion & Retention Modeling Can Improve Business Results

Insurance companies are well-known for investing their resources in GLM loss modelling. They have become so mainstream you’d be hard-pressed to find an actuary without any experience building them. In fact, nowadays, bigger companies even use their interns to keep these modelsRead More »
10
January

11 Best Practices for Building a Successful Production System

For us data scientists, the benefit of using statistical modeling and optimization techniques for our business is clear. Although we spend our days coding in SQL, python, R or other frameworks and our models are a software code or some nice mathematicalRead More »
31
July

Centralized Product Pricing in Your Bank – Overcoming the Obstacles

Imagine the following scenario: The pricing department in your bank decides on a new pricing policy for a mass market deposit product. Your bank has already realized that using a set pricelist and then allowing each branch/region to negotiate with its customersRead More »
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Machine Learning Survey 2017

This first-of-a-kind survey uncovers how insurance executives understand how the industry leverages Machine Learning today and how they expect Machine Learning to impact the industry in the near future.

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Open Banking: A Platform for Personalisation

Read the Earnix Open Banking report to learn how to prepare for the new open banking environment as well as see three Earnix open banking use cases.

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